Theoretical Results on Sparse Representations of Multiple-Measurement Vectors

The sparse representation of a multiple-measurement vector (MMV) is a relatively new problem in sparse representation. Efficient methods have been proposed. Although many theoretical results that are available in a simple case-single-measurement vector (SMV)-the theoretical analysis regarding MMV is lacking. In this paper, some known results of SMV are generalized to MMV. Some of these new results take advantages of additional information in the formulation of MMV. We consider the uniqueness under both an lscr 0 -norm-like criterion and an lscr 1 -norm-like criterion. The consequent equivalence between the lscr 0 -norm approach and the lscr 1 -norm approach indicates a computationally efficient way of finding…